Related to:
- Fig 3c-h
- Extended Data Fig 6b,c
- Extended Data Fig 7f
suppressPackageStartupMessages({
library(Seurat)
library(SeuratObject)
library(SummarizedExperiment)
library(ggplot2)
library(ggpubr)
library(tidyr)
library(patchwork)
library(viridis)
})
SAVEFIGS <- FALSE
TBO_seurat<-readRDS("../data/05_2025_RPM_RPMA_TBO_CellTag_Seurat_wSigs_FA_dpt_final.rds")
TBO_seurat
An object of class Seurat
110982 features across 26618 samples within 2 assays
Active assay: RNA (55491 features, 0 variable features)
2 layers present: counts, data
1 other assay present: norm
2 dimensional reductions calculated: umap, fa
clones <- readRDS("../data/05_2025_RPM_RPMA_TBOAllo_CellTagClones_Onlyclones.rds")
my_colors <- c(
"#E41A1C", # strong red
"#377EB8", # medium blue
"#4DAF4A", # green
"#984EA3", # purple
"#FF7F00", # orange
"#FFFF33", # yellow
"#A65628", # brown
"#e7298a", # pink
"#666666", # grey
"#66C2A5", # teal
"#FC8D62", # salmon
"#8DA0CB", # soft blue
"#E78AC3", # soft pink (different from 8)
"#A6D854", # light green (but yellowish tint, not green)
"#FFD92F", # lemon yellow
"#E5C494", # light brown
"#B3B3B3", # light grey
"#1B9E77", # deep teal
"#D95F02", # dark orange
"#7570B3", # strong purple
"#66A61E" # olive green (NOT same green as before)
)
pheno_col <- c("brown2","darkorchid4","dodgerblue","#66A61E","orange","turquoise4","turquoise")
names(pheno_col) <- c("NE","NE/Neuronal","Neuronal","ATOH1","Tuft","Triple-Neg","Basal")
Fig 3c
ForceAtlas embedding of RPM and RPMA CellTagged allografted tumor
cells
DimPlot(TBO_seurat, group.by='leiden_scVI_1.2', cols=my_colors, reduction='fa', label=TRUE, label.size=4) &
NoAxes() + theme(legend.position="none") # suppress legend

DimPlot(TBO_seurat, group.by='Pheno', cols=pheno_col, reduction='fa', label=FALSE, label.size=6) &
NoAxes()

# Extract coordinates from force-directed layout (or any layout)
dat <- Embeddings(TBO_seurat, reduction = "fa") %>%
as.data.frame() %>%
mutate(Cluster = TBO_seurat$leiden_scVI_1.2,
Genotype = TBO_seurat$Genotype)
# Set up your color vector (named)
my_colors_named <- setNames(my_colors, levels(TBO_seurat$leiden_scVI_1.2))
# Plot each Genotype with background cells greyed
plots <- lapply(unique(dat$Genotype), function(g) {
dat$highlight <- ifelse(dat$Genotype == g, "highlight", "background")
dat$Color <- ifelse(dat$Genotype == g, my_colors_named[dat$Cluster], "lightgrey")
ggplot(dat, aes(x = FA_1, y = FA_2)) +
geom_point(aes(color = Color), size = 0.5) +
scale_color_identity() +
ggtitle(g) +
theme_void() +
theme(
plot.title = element_text(hjust = 0.5),
legend.position = "none",
plot.background = element_rect(fill = "transparent", color = NA)
)
})
# Combine into one figure
a <- wrap_plots(plots, ncol = 2)
a

if(SAVEFIGS) ggsave("FAbyGeno.png", plot= a, width=6, height=3, dpi=300, bg = "transparent")
Visualizing Celltag/clone data
From table above, added whether clones were robust or not
Robust defined as >5 cells per clone post-QC.
CellTag metadata: clone information can also be found in Supplementary
Table 4 of Ireland et al, 2025
Idents(TBO_seurat) <- 'Robust'
table(TBO_seurat@meta.data$Robust, TBO_seurat@meta.data$UnID)
RPMA_Allo RPM_Allo3 RPM_Allo4 RPM_Allo_New
No 6659 5244 6523 2227
Robust 3501 1474 337 653
table(TBO_seurat@meta.data$Robust, TBO_seurat@meta.data$GenoCT)
RPMA_CTpreCre RPM_CTpostCre RPM_CTpreCre
No 6659 11767 2227
Robust 3501 1811 653
Identify robust clones
table(clones@meta.data$Robust, clones@meta.data$Genotype)
RPM RPMA
Robust 2464 3501
Assess clones by leiden cluster
Fig. 3d
First, just look at bar graph, no particular order #
Idents(clones) <- 'leiden_scVI_1.2'
x <- table(clones@meta.data$CellTag_Clone,Idents(clones))
proportions <- as.data.frame(100*prop.table(x, margin = 1))
# proportions$Cluster
colnames(proportions)<-c("Cluster", "Sample", "Frequency")
# Stacked
p <- ggplot(proportions, aes(fill=Sample, y=Frequency, x=Cluster)) +
geom_bar(position="stack", stat="identity")
p + scale_fill_manual(values=my_colors) +
theme_bw()+ theme(axis.text.y = element_text(size=20),
axis.text.x=element_text(size=14), axis.title.x =element_text(size=14),
axis.title.y = element_text(size=18), legend.text = element_text(size=12),
legend.title = element_text(size=18))+rotate_x_text(size=7,angle = 90)

dat <- p$data
Transform data to perform hierarchical (agglomerative) clustering
# Pivot to wide format: Cluster × Sample
mat <- dat %>%
pivot_wider(names_from = Sample, values_from = Frequency, values_fill = 0) %>%
tibble::column_to_rownames("Cluster") %>%
as.matrix()
mat_norm <- prop.table(mat, margin = 1) # normalize each row to sum to 1
Hierarchical (agglomerative) clustering of clones with default
parameters in pheatmap()
hm <- pheatmap::pheatmap(t(mat), cutree_rows = 1, cutree_cols = 8, cellwidth = 5,
cellheight = 5, fontsize = 8,
cluster_rows=FALSE, border_color=NA,
color = colorRampPalette(c("darkturquoise","black","red2"))(30))

clone_order <- colnames(t(mat))[hm$tree_col$order]
Fig 3d
Clustered clones by proportions of cells in Leiden clusters
# Now, plot clones by Leiden, ordered, final # (Fig. 3d)
Idents(clones)<-'leiden_scVI_1.2'
x <- table(clones@meta.data$CellTag_Clone,Idents(clones))
proportions <- as.data.frame(100*prop.table(x, margin = 1))
colnames(proportions) <- c("Cluster", "Sample", "Frequency")
proportions$Cluster <- factor(proportions$Cluster, clone_order)
# Stacked
p <- ggplot(proportions, aes(fill=Sample, y=Frequency, x=Cluster)) +
geom_bar(position="stack", stat="identity")
p + scale_fill_manual(values=my_colors) +
theme_bw() + theme(axis.text.y = element_text(size=20),
axis.text.x=element_text(size=14), axis.title.x =element_text(size=14),
axis.title.y = element_text(size=18), legend.text = element_text(size=12),
legend.title = element_text(size=18))+rotate_x_text(size=7,angle = 90) +
labs(x = NULL, y = "% of clone")

Idents(clones) <- 'Clone_Dynamics'
table(clones@meta.data$Clone_Dynamics)
Pattern_1 Pattern_2 Pattern_3 Pattern_4 Pattern_5 Unknown_1 Unknown_2
553 675 693 2797 1032 167 48
Function for plotting clone dynamics
clone_patterns <- c('Pattern_1', 'Pattern_2', 'Pattern_3', 'Pattern_4', 'Pattern_5', 'Unknown_1', 'Unknown_2')
pattern_colors <- c('orange', 'green2', 'red', 'royalblue2', 'purple', 'gray40', 'black')
names(pattern_colors) <- clone_patterns
plotCloneDyn <- function(clone_pattern, pattern_color, tit = "") {
# Get cells of interest
ss <- subset(clones, idents = clone_pattern)
highlighted_cells <- rownames(ss@meta.data)
# Plot all cells in grey, highlight the pattern in your chosen color
DimPlot(clones,
reduction = "fa",
cells.highlight = highlighted_cells,
sizes.highlight = 0.25,
cols.highlight = pattern_color,
cols = "grey90",
pt.size = 0.1) +
ggtitle(tit) & NoLegend() & NoAxes()
}
Fig 3e
ForceAtlas embedding showing representative clones of each pattern
with cells colored by pattern
cdp <- (lapply(1:7, function(i) plotCloneDyn(clone_patterns[i], pattern_colors[i], tit=clone_patterns[i])))
p_combined <- ggarrange(plotlist = cdp, ncol = 4, nrow = 2)
p_combined

getCloneCellIDs <- function(id) {
cellIDs <- rownames(subset(clones,idents=c(id))@meta.data)
}
clone_cell_ids <- lapply(setNames(clone_patterns, clone_patterns), getCloneCellIDs)
plotPhenoWithBackground <- function(clone_pattern, pheno_col, title = "", seurat_obj = clones) {
# Check if 'fa' reduction exists
if (!"fa" %in% names(seurat_obj@reductions)) {
stop("The 'fa' reduction is not found in the Seurat object.")
}
# Extract FA coordinates and metadata
coords <- as.data.frame(Embeddings(seurat_obj, reduction = "fa"))
coords$cell <- rownames(coords)
# Get metadata
meta <- seurat_obj@meta.data[, c("Clone_Dynamics", "Pheno")]
meta$cell <- rownames(meta)
# Merge coordinates and metadata
dat <- merge(coords, meta, by = "cell")
# Flag highlighted cells
dat$highlight <- dat$Clone_Dynamics == clone_pattern
dat$Pheno <- droplevels(dat$Pheno)
# Split background and foreground
bg_dat <- dat[!dat$highlight, ]
fg_dat <- dat[dat$highlight, ]
if (nrow(fg_dat) == 0) {
warning(paste("No cells found for Clone_Dynamics pattern:", clone_pattern))
return(ggplot() + ggtitle(paste(clone_pattern, "(no cells)")))
}
# Plot
p <- ggplot() +
geom_point(data = bg_dat, aes(x = FA_1, y = FA_2), color = "gray90", size = 0.2) +
geom_point(data = fg_dat, aes(x = FA_1, y = FA_2, color = Pheno), size = 1) +
scale_color_manual(values = pheno_col, drop = FALSE) +
ggtitle(title) +
theme_void() +
theme(
legend.position = "none",
panel.background = element_rect(fill = "transparent", color = NA),
plot.background = element_rect(fill = "transparent", color = NA)
)
return(p)
}
ForceAtlas embedding showing cells colored by SCLC phenotype
DimPlot(clones, group.by='Pheno', cols=pheno_col, reduction='fa', label=FALSE, label.size=6, pt.size = 0.05) &
NoAxes()

Fig 3f
ForceAtlas embedding showing representative clones of each pattern
with cells colored by SCLC phenotype
plots <- lapply(clone_patterns, function(pat) plotPhenoWithBackground(pat, pheno_col, title = pat))
p_combined <- ggarrange(plotlist = plots, ncol = 4, nrow = 2)
p_combined

Visualize individual clones
Ext Data Fig. 6b,c
plotPatternDynByClone <- function(clone_pattern) {
Idents(clones)<-'Clone_Dynamics'
p1 <- subset(clones,idents=c(clone_pattern))
pattern_color <- pattern_colors[clone_pattern]
test <- as.data.frame(p1$CellTag_Clone)
test$Barcodes <- rownames(test)
# Group by CellTag_Clone
test <- test %>% group_by(p1$CellTag_Clone)
test <- dplyr::group_split(test)
n_clones <- length(test)
# Plot in for loop all RPM clones in Pattern 1
plot_lst <- vector("list", length = n_clones)
for (i in seq(n_clones)) {
g <- DimPlot(TBO_seurat, group.by="CellTag_Clone", reduction='fa', order=TRUE,
cells.highlight=test[[i]]$Barcodes,sizes.highlight=2,
cols.highlight=pattern_color) +
ggtitle(paste0(test[[i]]$`p1$CellTag_Clone`[1])) +
theme(plot.title = element_text(size = 8,face = "plain")) & NoLegend() &
NoAxes()
plot_lst[[i]] <- g
}
# Combine multiple plots for output, as desired
return(cowplot::plot_grid(plotlist = plot_lst, ncol=5))
}
pattern_plot_list <- lapply(clone_patterns, plotPatternDynByClone)
n_plots_per_pattern <- sapply(pattern_plot_list, function(x) length(x[['layers']]))
## calculate height of figure based on number of plots per pattern
plt_height <- 2.667 * ((n_plots_per_pattern %/% 5) + 1)
Ext Data Fig 6b,c
pattern_plot_list[[1]]

pattern_plot_list[[2]]

pattern_plot_list[[3]]

pattern_plot_list[[4]]

pattern_plot_list[[5]]

pattern_plot_list[[6]]

pattern_plot_list[[7]]

Visualize only clones matching in vivo in FA projection
Ext Data Fig. 7f
Idents(clones) <- "CellTag_Clone"
# Subset just the clones that match the in vitro, pattern 1
invitromatch <- subset(clones, idents=c("RPM_Clone_14","RPM_Clone_2","RPM_Clone_33","RPM_Clone_36","RPM_Clone_6"))
p1_cells <- colnames(invitromatch)
p2_cells <- colnames(subset(clones, idents="RPM_Clone_23"))
p5_cells <- colnames(subset(clones, idents="RPM_Clone_13"))
DimPlot(TBO_seurat, group.by="CellTag_Clone", reduction='fa',
order=TRUE, cells.highlight=p1_cells, sizes.highlight=2,
cols.highlight=c("orange")) + ggtitle("") & NoLegend() & NoAxes()

DimPlot(TBO_seurat, group.by="CellTag_Clone", reduction='fa',
order=TRUE, cells.highlight=p2_cells, sizes.highlight=2,
cols.highlight=pattern_colors["Pattern_2"]) + ggtitle("") & NoLegend() & NoAxes()

DimPlot(TBO_seurat, group.by="CellTag_Clone", reduction='fa',
order=TRUE, cells.highlight=p5_cells, sizes.highlight=2,
cols.highlight=pattern_colors["Pattern_5"]) + ggtitle("") & NoLegend() & NoAxes()

NA
NA
Fig. 3g
dpt psuedotime in FA space
FeaturePlot(TBO_seurat, features = c("dpt_pseudotime"), pt.size=0.01,
reduction='fa',) + scale_color_viridis(option="viridis",direction=-1)& NoAxes()

Function to plot clone dynamics colored by DPT
plotCloneDynDpt <- function(clone_pattern, seurat_obj, title = "") {
# Subset cells of interest
ss <- subset(seurat_obj, idents = clone_pattern)
highlight_cells <- colnames(ss)
# Extract embeddings and metadata
fa_coords <- Embeddings(seurat_obj, "fa")
dpt_vals <- seurat_obj@meta.data$dpt_pseudotime
names(dpt_vals) <- rownames(seurat_obj@meta.data)
dat <- as.data.frame(fa_coords)
dat$highlight <- ifelse(rownames(dat) %in% highlight_cells, "yes", "no")
dat$pseudotime <- dpt_vals[rownames(dat)]
colnames(dat)[1:2] <- c("FA1", "FA2") # name columns for clarity
library(ggplot2)
ggplot(dat, aes(x = FA1, y = FA2)) +
# Background cells
geom_point(data = subset(dat, highlight == "no"),
color = "grey85", size = 0.1) +
# Highlighted cells colored by pseudotime
geom_point(data = subset(dat, highlight == "yes"),
aes(color = pseudotime), size = 0.25) +
scale_color_viridis_c(option = "turbo", direction = -1) +
ggtitle(title) +
theme_void() +
theme(
legend.position = "none",
panel.border = element_blank(), # removes any panel border
plot.background = element_rect(fill = "transparent", color = NA),
panel.background = element_rect(fill = "transparent", color = NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
}
Fig 3h
Idents(clones) <- 'Clone_Dynamics'
my_pats <- c('Pattern_1','Pattern_2','Pattern_5','Unknown_1','Pattern_3','Pattern_4')
plots <- lapply(my_pats, function(pat) {
tit <- gsub(pat, '_', ' ')
plotCloneDynDpt(pat, clones, title = tit)
})
cowplot::plot_grid(plotlist = plots, ncol=6)

---
title: "RPM_RPMA_Allos_CellTag notebook"
author: "Abbie Ireland, Darren Tyson"
date: "2025-06-24"
output: html_notebook
---

### Modifying original code
Original file: [Fig3_4_6_ExtFig5-6-10_RPM_RPMA_Allos_CellTag.R](https://github.com/TGOliver-lab/Ireland_Basal_SCLC_2025/blob/main/R_Code/Fig3_4_6_ExtFig5-6-10_RPM_RPMA_Allos_CellTag.R)  

Only CellTag analysis included in this notebook.

### Related to:
* Fig 3c-h
* Extended Data Fig 6b,c
* Extended Data Fig 7f

```{r}
suppressPackageStartupMessages({
    library(Seurat)
    library(SeuratObject)
    library(SummarizedExperiment)
    library(ggplot2)
    library(ggpubr)
    library(tidyr)
    library(patchwork)
    library(viridis)
})
```
```{r}
SAVEFIGS <- FALSE
```


```{r}
TBO_seurat<-readRDS("../data/05_2025_RPM_RPMA_TBO_CellTag_Seurat_wSigs_FA_dpt_final.rds")
TBO_seurat
```

```{r}
clones <- readRDS("../data/05_2025_RPM_RPMA_TBOAllo_CellTagClones_Onlyclones.rds")
```

```{r}
my_colors <- c(
  "#E41A1C", # strong red
  "#377EB8", # medium blue
  "#4DAF4A", # green
  "#984EA3", # purple
  "#FF7F00", # orange
  "#FFFF33", # yellow
  "#A65628", # brown
  "#e7298a", # pink
  "#666666", # grey
  "#66C2A5", # teal
  "#FC8D62", # salmon
  "#8DA0CB", # soft blue
  "#E78AC3", # soft pink (different from 8)
  "#A6D854", # light green (but yellowish tint, not green)
  "#FFD92F", # lemon yellow
  "#E5C494", # light brown
  "#B3B3B3", # light grey
  "#1B9E77", # deep teal
  "#D95F02", # dark orange
  "#7570B3", # strong purple
  "#66A61E"  # olive green (NOT same green as before)
)

pheno_col <- c("brown2","darkorchid4","dodgerblue","#66A61E","orange","turquoise4","turquoise")
names(pheno_col) <- c("NE","NE/Neuronal","Neuronal","ATOH1","Tuft","Triple-Neg","Basal")
```

#### Fig 3c
ForceAtlas embedding of RPM and RPMA CellTagged allografted tumor cells
```{r fig.height=3, fig.width=3, message=FALSE, warning=FALSE}
DimPlot(TBO_seurat, group.by='leiden_scVI_1.2', cols=my_colors, reduction='fa', label=TRUE, label.size=4) & 
    NoAxes() + theme(legend.position="none") # suppress legend

```

```{r fig.height=3, fig.width=4.5, message=FALSE, warning=FALSE}
DimPlot(TBO_seurat, group.by='Pheno', cols=pheno_col, reduction='fa', label=FALSE, label.size=6) & 
    NoAxes()
```

```{r fig.height=3, fig.width=6, message=FALSE, warning=FALSE}
# Extract coordinates from force-directed layout (or any layout)
dat <- Embeddings(TBO_seurat, reduction = "fa") %>% 
  as.data.frame() %>%
  mutate(Cluster = TBO_seurat$leiden_scVI_1.2,
         Genotype = TBO_seurat$Genotype)

# Set up your color vector (named)
my_colors_named <- setNames(my_colors, levels(TBO_seurat$leiden_scVI_1.2))

# Plot each Genotype with background cells greyed
plots <- lapply(unique(dat$Genotype), function(g) {
  dat$highlight <- ifelse(dat$Genotype == g, "highlight", "background")
  dat$Color <- ifelse(dat$Genotype == g, my_colors_named[dat$Cluster], "lightgrey")

  ggplot(dat, aes(x = FA_1, y = FA_2)) +
    geom_point(aes(color = Color), size = 0.5) +
    scale_color_identity() +
    ggtitle(g) +
    theme_void() +
    theme(
      plot.title = element_text(hjust = 0.5),
      legend.position = "none",
      plot.background = element_rect(fill = "transparent", color = NA)
    )
})

# Combine into one figure
a <- wrap_plots(plots, ncol = 2)
a

if(SAVEFIGS) ggsave("FAbyGeno.png", plot= a, width=6, height=3, dpi=300, bg = "transparent")
```



### Visualizing Celltag/clone data

From table above, added whether clones were robust or not  
Robust defined as >5 cells per clone post-QC.  
CellTag metadata: clone information can also be found in Supplementary Table 4 of Ireland et al, 2025  
```{r}
Idents(TBO_seurat) <- 'Robust'
```


```{r}
table(TBO_seurat@meta.data$Robust, TBO_seurat@meta.data$UnID)
table(TBO_seurat@meta.data$Robust, TBO_seurat@meta.data$GenoCT)
```
### Identify robust clones
```{r}
table(clones@meta.data$Robust, clones@meta.data$Genotype)
```

### Assess clones by leiden cluster
#### Fig. 3d  
First, just look at bar graph, no particular order #

```{r fig.height=4, fig.width=10}
Idents(clones) <- 'leiden_scVI_1.2'

x <- table(clones@meta.data$CellTag_Clone,Idents(clones))
proportions <- as.data.frame(100*prop.table(x, margin = 1))

# proportions$Cluster
colnames(proportions)<-c("Cluster", "Sample", "Frequency")

# Stacked
p <- ggplot(proportions, aes(fill=Sample, y=Frequency, x=Cluster)) + 
  geom_bar(position="stack", stat="identity")

p + scale_fill_manual(values=my_colors) + 
  theme_bw()+ theme(axis.text.y = element_text(size=20), 
                    axis.text.x=element_text(size=14), axis.title.x =element_text(size=14), 
                    axis.title.y = element_text(size=18), legend.text = element_text(size=12), 
                    legend.title = element_text(size=18))+rotate_x_text(size=7,angle = 90)

dat <- p$data
```
Transform data to perform hierarchical (agglomerative) clustering
```{r}
# Pivot to wide format: Cluster × Sample
mat <- dat %>%
  pivot_wider(names_from = Sample, values_from = Frequency, values_fill = 0) %>%
  tibble::column_to_rownames("Cluster") %>%
  as.matrix()

mat_norm <- prop.table(mat, margin = 1)  # normalize each row to sum to 1
```

Hierarchical (agglomerative) clustering of clones with default parameters in `pheatmap()`
```{r fig.height=5, fig.width=8}
hm <- pheatmap::pheatmap(t(mat), cutree_rows = 1, cutree_cols = 8, cellwidth = 5, 
                         cellheight = 5, fontsize = 8,
                         cluster_rows=FALSE, border_color=NA, 
                         color = colorRampPalette(c("darkturquoise","black","red2"))(30))

clone_order <- colnames(t(mat))[hm$tree_col$order]
```


#### Fig 3d
Clustered clones by proportions of cells in Leiden clusters
```{r fig.height=7, fig.width=10}
# Now, plot clones by Leiden, ordered, final # (Fig. 3d)
Idents(clones)<-'leiden_scVI_1.2'

x <- table(clones@meta.data$CellTag_Clone,Idents(clones))
proportions <- as.data.frame(100*prop.table(x, margin = 1))

colnames(proportions) <- c("Cluster", "Sample", "Frequency")
proportions$Cluster <- factor(proportions$Cluster, clone_order)

# Stacked
p <- ggplot(proportions, aes(fill=Sample, y=Frequency, x=Cluster)) + 
    geom_bar(position="stack", stat="identity")

p + scale_fill_manual(values=my_colors) + 
  theme_bw() + theme(axis.text.y = element_text(size=20), 
                     axis.text.x=element_text(size=14), axis.title.x =element_text(size=14), 
                     axis.title.y = element_text(size=18), legend.text = element_text(size=12), 
                     legend.title = element_text(size=18))+rotate_x_text(size=7,angle = 90) + 
    labs(x = NULL, y = "% of clone")

```

```{r}
Idents(clones) <- 'Clone_Dynamics'
table(clones@meta.data$Clone_Dynamics)
```

### Function for plotting clone dynamics

```{r}
clone_patterns <- c('Pattern_1', 'Pattern_2', 'Pattern_3', 'Pattern_4', 'Pattern_5', 'Unknown_1', 'Unknown_2')
pattern_colors <- c('orange', 'green2', 'red', 'royalblue2', 'purple', 'gray40', 'black')
names(pattern_colors) <- clone_patterns
```

```{r}
plotCloneDyn <- function(clone_pattern, pattern_color, tit = "") {
    # Get cells of interest
    ss <- subset(clones, idents = clone_pattern)
    highlighted_cells <- rownames(ss@meta.data)
    
    # Plot all cells in grey, highlight the pattern in your chosen color
    DimPlot(clones, 
            reduction = "fa", 
            cells.highlight = highlighted_cells,
            sizes.highlight = 0.25,
            cols.highlight = pattern_color,
            cols = "grey90", 
            pt.size = 0.1) + 
    ggtitle(tit) & NoLegend() & NoAxes()
}
```


#### Fig 3e
ForceAtlas embedding showing representative clones of each pattern with cells colored by pattern
```{r fig.height=4, fig.width=8, message=FALSE, warning=FALSE}
cdp <- (lapply(1:7, function(i) plotCloneDyn(clone_patterns[i], pattern_colors[i], tit=clone_patterns[i])))
p_combined <- ggarrange(plotlist = cdp, ncol = 4, nrow = 2)
p_combined
```

```{r}
getCloneCellIDs <- function(id) {
    cellIDs <- rownames(subset(clones,idents=c(id))@meta.data)
}
clone_cell_ids <- lapply(setNames(clone_patterns, clone_patterns), getCloneCellIDs)
```


```{r}
plotPhenoWithBackground <- function(clone_pattern, pheno_col, title = "", seurat_obj = clones) {
    # Check if 'fa' reduction exists
    if (!"fa" %in% names(seurat_obj@reductions)) {
        stop("The 'fa' reduction is not found in the Seurat object.")
    }

    # Extract FA coordinates and metadata
    coords <- as.data.frame(Embeddings(seurat_obj, reduction = "fa"))
    coords$cell <- rownames(coords)

    # Get metadata
    meta <- seurat_obj@meta.data[, c("Clone_Dynamics", "Pheno")]
    meta$cell <- rownames(meta)

    # Merge coordinates and metadata
    dat <- merge(coords, meta, by = "cell")

    # Flag highlighted cells
    dat$highlight <- dat$Clone_Dynamics == clone_pattern
    dat$Pheno <- droplevels(dat$Pheno)

    # Split background and foreground
    bg_dat <- dat[!dat$highlight, ]
    fg_dat <- dat[dat$highlight, ]

    if (nrow(fg_dat) == 0) {
        warning(paste("No cells found for Clone_Dynamics pattern:", clone_pattern))
        return(ggplot() + ggtitle(paste(clone_pattern, "(no cells)")))
    }

    # Plot
    p <- ggplot() +
        geom_point(data = bg_dat, aes(x = FA_1, y = FA_2), color = "gray90", size = 0.2) +
        geom_point(data = fg_dat, aes(x = FA_1, y = FA_2, color = Pheno), size = 1) +
        scale_color_manual(values = pheno_col, drop = FALSE) +
        ggtitle(title) +
        theme_void() +
        theme(
            legend.position = "none",
            panel.background = element_rect(fill = "transparent", color = NA),
            plot.background = element_rect(fill = "transparent", color = NA)
        )

    return(p)
}
```

ForceAtlas embedding showing cells colored by SCLC phenotype
```{r fig.height=3, fig.width=4.5}
DimPlot(clones, group.by='Pheno', cols=pheno_col, reduction='fa', label=FALSE, label.size=6, pt.size = 0.05) & 
    NoAxes()
```


#### Fig 3f
ForceAtlas embedding showing representative clones of each pattern with cells colored by SCLC phenotype
```{r fig.height=4, fig.width=8, message=FALSE, warning=FALSE}
plots <- lapply(clone_patterns, function(pat) plotPhenoWithBackground(pat, pheno_col, title = pat))
p_combined <- ggarrange(plotlist = plots, ncol = 4, nrow = 2)
p_combined
```


### Visualize individual clones
Ext Data Fig. 6b,c
```{r}
plotPatternDynByClone <- function(clone_pattern) {
    Idents(clones)<-'Clone_Dynamics'
    p1 <- subset(clones,idents=c(clone_pattern))
    
    pattern_color <- pattern_colors[clone_pattern]

    test <- as.data.frame(p1$CellTag_Clone)
    test$Barcodes <- rownames(test)
    # Group by CellTag_Clone
    test <- test %>% group_by(p1$CellTag_Clone)
    test <- dplyr::group_split(test)
    
    n_clones <- length(test)
    # Plot in for loop all RPM clones in Pattern 1
    plot_lst <- vector("list", length = n_clones)
    for (i in seq(n_clones)) {
      g <- DimPlot(TBO_seurat, group.by="CellTag_Clone", reduction='fa', order=TRUE, 
                   cells.highlight=test[[i]]$Barcodes,sizes.highlight=2, 
                   cols.highlight=pattern_color) +
          ggtitle(paste0(test[[i]]$`p1$CellTag_Clone`[1])) + 
          theme(plot.title = element_text(size = 8,face = "plain")) & NoLegend() & 
          NoAxes()
      plot_lst[[i]] <- g
    }
    
    # Combine multiple plots for output, as desired
    return(cowplot::plot_grid(plotlist = plot_lst, ncol=5))
}
```

```{r}
pattern_plot_list <- lapply(clone_patterns, plotPatternDynByClone)

n_plots_per_pattern <- sapply(pattern_plot_list, function(x) length(x[['layers']]))

## calculate height of figure based on number of plots per pattern
plt_height <- 2.667 * ((n_plots_per_pattern %/% 5) + 1)
```

#### Ext Data Fig 6b,c
```{r fig.height=16, fig.width=12}
pattern_plot_list[[1]]
```


```{r fig.height=5.33, fig.width=12}
pattern_plot_list[[2]]
```
```{r fig.height=10.667, fig.width=12}
pattern_plot_list[[3]]
```
```{r fig.height=13.333, fig.width=12}
pattern_plot_list[[4]]
```

```{r fig.height=2.667, fig.width=12}
pattern_plot_list[[5]]
```

```{r fig.height=5.33, fig.width=12}
pattern_plot_list[[6]]
```

```{r fig.height=2.667, fig.width=12}
pattern_plot_list[[7]]
```
## Visualize only clones matching in vivo in FA projection
Ext Data Fig. 7f

```{r fig.height=3, fig.width=3}
Idents(clones) <- "CellTag_Clone"
# Subset just the clones that match the in vitro, pattern 1
invitromatch <- subset(clones, idents=c("RPM_Clone_14","RPM_Clone_2","RPM_Clone_33","RPM_Clone_36","RPM_Clone_6"))

p1_cells <- colnames(invitromatch)
p2_cells <- colnames(subset(clones, idents="RPM_Clone_23"))
p5_cells <- colnames(subset(clones, idents="RPM_Clone_13"))

DimPlot(TBO_seurat, group.by="CellTag_Clone", reduction='fa', 
        order=TRUE, cells.highlight=p1_cells, sizes.highlight=2, 
        cols.highlight=c("orange")) + ggtitle("") & NoLegend() & NoAxes()
DimPlot(TBO_seurat, group.by="CellTag_Clone", reduction='fa', 
        order=TRUE, cells.highlight=p2_cells, sizes.highlight=2, 
        cols.highlight=pattern_colors["Pattern_2"]) + ggtitle("") & NoLegend() & NoAxes()
DimPlot(TBO_seurat, group.by="CellTag_Clone", reduction='fa', 
        order=TRUE, cells.highlight=p5_cells, sizes.highlight=2, 
        cols.highlight=pattern_colors["Pattern_5"]) + ggtitle("") & NoLegend() & NoAxes()


```

```{r}

```


#### Fig. 3g 
dpt psuedotime in FA space 
```{r fig.height=3, fig.width=3.75, message=FALSE, warning=FALSE}
FeaturePlot(TBO_seurat, features = c("dpt_pseudotime"), pt.size=0.01,
            reduction='fa',) + scale_color_viridis(option="viridis",direction=-1)& NoAxes()

```
#### Function to plot clone dynamics colored by DPT
```{r}
plotCloneDynDpt <- function(clone_pattern, seurat_obj, title = "") {
    # Subset cells of interest
    ss <- subset(seurat_obj, idents = clone_pattern)
    highlight_cells <- colnames(ss)

    # Extract embeddings and metadata
    fa_coords <- Embeddings(seurat_obj, "fa")
    dpt_vals <- seurat_obj@meta.data$dpt_pseudotime
    names(dpt_vals) <- rownames(seurat_obj@meta.data)

    dat <- as.data.frame(fa_coords)
    dat$highlight <- ifelse(rownames(dat) %in% highlight_cells, "yes", "no")
    dat$pseudotime <- dpt_vals[rownames(dat)]
    colnames(dat)[1:2] <- c("FA1", "FA2")  # name columns for clarity

    library(ggplot2)
    ggplot(dat, aes(x = FA1, y = FA2)) +
        # Background cells
        geom_point(data = subset(dat, highlight == "no"), 
                   color = "grey85", size = 0.1) +
        # Highlighted cells colored by pseudotime
        geom_point(data = subset(dat, highlight == "yes"), 
                   aes(color = pseudotime), size = 0.25) +
        scale_color_viridis_c(option = "turbo", direction = -1) +
        ggtitle(title) +
        theme_void() +
        theme(
            legend.position = "none",
            panel.border = element_blank(),      # removes any panel border
            plot.background = element_rect(fill = "transparent", color = NA),
            panel.background = element_rect(fill = "transparent", color = NA),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank()
        )
}
```



#### Fig 3h
```{r fig.height=2, fig.width=12, message=FALSE, warning=FALSE}
Idents(clones) <- 'Clone_Dynamics'

my_pats <- c('Pattern_1','Pattern_2','Pattern_5','Unknown_1','Pattern_3','Pattern_4')

plots <- lapply(my_pats, function(pat) {
    tit <- gsub(pat, '_', ' ')
    plotCloneDynDpt(pat, clones, title = tit)
})

cowplot::plot_grid(plotlist = plots, ncol=6)
```

